As the number of users viewing information and purchasing items electronically increases, there is a corresponding increase in the amount of advertising revenue spent in electronic environments. In some cases, advertisements are specifically selected for certain pages or other interfaces displayed to a user. In other cases, these advertisements are selected based on content that can be displayed in any of a number of different pages. For example, a user might search for information about a keyword through a search engine. When a results page is returned to the user that includes search results relating to that keyword, at least one advertisement can be included with the results page that relates to the keyword and/or search results. Often, the advertisement includes a hypertext link or other user-selectable element that enables the user to navigate to another page or display relating to the advertisement.
The selection of the content to include with the advertisement, such as text, graphics, audio, and/or video for the advertisement, as well as the page to which the user may navigate (hereinafter referred to as the “landing page”), should be determined such that the user viewing the advertisement on the search results page not only will be interested in following the link, but in the case of advertising an item, also will be likely to buy or otherwise consume the advertised item. In conventional systems, generic templates were selected for categories that included a number of keywords, such that whenever one of those keywords was submitted by a user, an advertisement for a general category would be shown, which might not be very relevant to the actual keyword that was submitted. Such an approach does not take into account various factors that can increase revenue, conversions, etc., or otherwise result in a dynamic selection of an optimal advertisement to display to a user.
Further, in many cases there will be multiple advertisers bidding or otherwise attempting to have their advertisement(s) displayed for a given keyword. In such a case, it can be desirable for an advertiser to accurately determine the amount of money the advertiser should bid for any given keyword. When there is not enough data available to make such a decision, conventional systems might simply rely on an average or other default bid amount. For electronic marketplaces or other large advertisers where there may be millions of keywords, making more accurate estimates of the amount to bid for each keyword can result in significant savings and/or additional revenue for the advertiser.